R Notebook

This file will take you through recreating the figures presented in the manuscript. Note that some of the figures went through additional edits in Illustrator for additional labelling!

Preparation

Load packages

library(dplyr)
library(ggplot2)
library(forcats)
library(here)
library(deSolve)
library(crone)
library(optimParallel)
library(doParallel)
library(doRNG)
library(arrow)
library(stringr)
library(parallel)
library(ggpubr)
library(scales)
library(bayestestR)
library(purrr)
library(tidyr)
library(splines2)
library(splancs)

load variables

parameters_tsukushi <- c(R1 = 8.89*10^6, 
                lambda = 3.7*10^5,
                mu = 0.025, 
                p = 8*10^-6, 
                alpha = 1, 
                alphag = 2, 
                beta = 5.721, 
                mum = 48, 
                mug = 4, 
                I0 = 43.85965, 
                Ig0 = 0, 
                a = 150, 
                b = 100, 
                sp = 1,
                psin = 16.69234,
                psiw = 0.8431785,
                phin = 0.03520591, 
                phiw = 550.842,
                iota = 2.18*(10^6),
                rho = 0.2627156)

# import in data files
ez_label <- read.csv(here("code_repository/data/ez_label.csv")) ## labelling schene
si_opt.df <- read.csv(here("code_repository/data/si_opt.csv")) ## optimized parameter + fitness list
si_dyn.df <- read_parquet(here("code_repository/data/si_dyn.parquet")) ## dynamics of single cue models
si_rn.df <-  read_parquet(here("code_repository/data/si_rn.parquet")) ## reaction norms of single cue models
si_rug.df <- read_parquet(here("code_repository/data/si_rug.parquet")) ## data for rug plots
mc_all_fitness.df <- read_parquet(here("code_repository/data/mc_all_fitness.parquet")) ## fitness values for mc when all parameters are varying
mc_single_fitness.df <- read_parquet(here("code_repository/data/mc_single_fitness.parquet")) ## fitness values for mc when only one parameter is varying
dual_cue_f_lc.df <- read.csv(here("code_repository/data/dual_cue_fitness_local.csv")) ## optimized dual cue strategy using L-BFGS-B
dual_cue_f_glb.df <- read.csv(here("code_repository/data/dual_cue_fitness_global.csv")) ## optimized dual cue strategy using DEoptim + L-BFG
dual_cue_f_final.df <- read.csv(here("code_repository/data/dual_cue_fitness_final.csv")) ## final dataframe containing strategy that produced highest fitness (dual cue models)
dual_selected_cr.df <- read_parquet(here("code_repository/data/dual_selected_cr.parquet")) ## cr dynamics of selected models
cue_range_si_alt.df <- read.csv(here("code_repository/data/cue_range_si_alt.csv")) ## cue ranges for dual cue models
dual_cue_dyn.df <- read_parquet(here("code_repository/data/dual_cue_dyn.parquet")) ## dynamics of dual cue models
exp_ss.df <- read.csv(here("code_repository/data/experimental_data.csv")) ## cleaned experimental records of P. chabaudi infection
posterior.df <- read.csv(here("code_repository/data/posterior.csv")) ## posterior distribution of different parameter values
mc_burst.df <- read.csv(here("code_repository/data/mc_burst_opt.csv")) ## fitness values of cues when optimized with different beta values
mc_burst_single_input.df <- read.csv(here("code_repository/data/mc_burst_single_input.csv")) ## iput dataframe for simulating dyamics for for models optimized with different burst values (single cues only)
mc_burst_dual_input.df <- read.csv(here("code_repository/data/mc_burst_dual_input.csv")) ## ditto for R log + I log
mc_burst_final_diff.df <- read.csv(here("code_repository/data/mc_burst_final_diff.csv")) ## fitness of parasites adopting different parameters optimized for diff burst size but simulated with discordant burst size 
mc_burst_rn.df <- read_parquet(here("code_repository/data/mc_burst_rn.parquet")) ## dataframe containing reaction norms of strains optimzied at various burst size filtered by relevant ranges (rug)
mc_burst_rn_RI.df <- read_parquet(here("code_repository/data/mc_burst_rn_RI.parquet")) ## dataframe containing reaction norms of strains optimzied at various burst size filtered by relevant ranges (rug)
mc_burst_rug.df_f <- read_parquet(here("code_repository/data/mc_burst_rug_f.parquet")) ## rug dataframe for parasites adopting the ideal strategy for various burst sizes
mc_burst_RI_rug.df <- read_parquet(here("code_repository/data/mc_burst_RI_rug.parquet")) ## dito but for R log and I log
mc_burst_dyn.df <- read_parquet(here("code_repository/data/mc_burst_dyn.parquet")) ## dynamics data for mc burst
si_dyn_30.df <- read_parquet(here("code_repository/data/si_dyn_30.parquet")) ## single cue dynamics data (30 days)
dual_cue_dyn_30.df <- read_parquet(here("code_repository/data/dual_cue_dyn_30.parquet")) ## dual cue dynamics (30 days)

## validation data (comparing fitness to random spline)
validation.ls <- list.files(here("code_repository/data/si_validation/"), full.names = T)
validation.df <- do.call(rbind, lapply(validation.ls, read.csv))

# import in code
source(here("code_repository/functions/chabaudi_si_clean.R"))
source(here("code_repository/functions/par_to_df.R"))
source(here("code_repository/functions/chabaudi_si_clean_high.R"))
source(here("code_repository/functions/par_to_hm.R"))
source(here("code_repository/functions/par_to_hm_te.R"))

# color codes
orange <- "#fc8d59"
blue <- "#4575b4"

#=================================# # Cue perception mediates fitness #=================================# #———— A. conversion rate dynamics and fitness values ————# ## function for dynamics generation

# function for getting single cue infection dynamics
get_si_dyn <- function(df){
  ## processing model input
  par <- c(df$var1, df$var2, df$var3, df$var4) ## parameters
  cue <- df$cue ## cue choice
  log <- ifelse(df$log=="log", "log10", "none") ## log or not
  cue_range <- seq(df$low, df$high, by = df$by) ## cue range
  id <- df$id ## id
  
  ## get dynamics data
  dyn <- chabaudi_si_clean(
            parameters_cr = par, 
            parameters = parameters_tsukushi, 
            time_range = seq(0, 20, 0.001), 
            cue = cue, 
            cue_range = cue_range, 
            log_cue = log,
            immunity = "tsukushi",
            solver = "vode",
            dyn = TRUE)
  
  # append id
  dyn2 <- cbind(dyn, id = rep(id, nrow(dyn)))
  
  # return results
 return(dyn2)
}

run function to generate data

## split si_opt into list
si_opt.ls <- split(si_opt.df, seq(nrow(si_opt.df)))

## get dynamics
si_dyn <- mclapply(si_opt.ls, get_si_dyn, mc.cores = 8)

## combine the dynamics file
si_dyn.df <- do.call(rbind, si_dyn)

## save
# write_parquet(si_dyn.df, here("code_repository/data/si_dyn.parquet"))

get 30 day simulation data (not used for fitness but for plotting purposes)

## get dynamics
si_opt.ls2 <- split(si_opt.df %>% filter(cue != "t"), seq(nrow(si_opt.df%>% filter(cue != "t"))))
si_dyn_30 <- mclapply(si_opt.ls2, get_si_dyn, mc.cores = 8)

## combine the dynamics file
si_dyn_30.df <- do.call(rbind, si_dyn_30)
si_dyn_30.df %>% group_by(id) %>% summarise(max_time = max(time))

## save
write_parquet(si_dyn_30.df, here("code_repository/data/si_dyn_30.parquet"))

process data to isolate conversion rate and pair them with fitness

si_opt.df[si_opt.df$cue == "t",]$fitness_20

[1] 9.7879

plot

## plot together
fitness_rank_cr.plt <- ggarrange(fitness_rank.plt, fitness_cr.plt, widths = c(1.2, 1), align = "h", common.legend = T,
                                 legend = "bottom")

Warning: Removed 11022 rows containing missing values or values outside the scale range (`geom_raster()`).
Warning: Removed 11022 rows containing missing values or values outside the scale range (`geom_raster()`).

#———— B. Reaction norms ————# ## function to obtain the cue values sensed by parasites from dynamics datas

get_rug <- function(df){
  ## process cue
  cue <- unique(df$cue)
  
  ## if cue contains "+", we need to first split them up and add them into the final dataframe
   if(stringr::str_detect(cue, "\\+")){
    cue_split <- stringr::str_split(string = cue, pattern = "\\+", simplify = T)
    ## get the two cues 
    cue_temp_1 <- cue_split[[1]]
    cue_temp_2 <- cue_split[[2]]
    ## filter dyn
    rug <- df %>% 
      filter(variable == cue_temp_1 | variable == cue_temp_2) %>% 
      dplyr::group_by(time) %>% 
      dplyr::mutate(sum = sum(value, na.rm = T)) %>% 
      select(time, value = sum, id)
  }
  
  # for cue with no addition, it is simply filtering for the values and returning it
  if(stringr::str_detect(cue, "\\+", negate = T)){
    rug <- df %>% 
      dplyr::filter(variable == cue) %>% 
      dplyr::select(time, value, id)}
  
  return(rug)
}

run function to obtain cue values

# join dynamics data with cue information
si_dyn_cue.df <- left_join(si_dyn.df, si_opt.df, by = "id")

# split based on individual labels
si_dyn_cue.ls <- si_dyn_cue.df %>% group_split(id)

# run function to get rug
si_rug <- mclapply(si_dyn_cue.ls, get_rug, mc.cores = 6)

# combine and save
si_rug.df <- do.call(rbind, si_rug)
write_parquet(si_rug.df, here("code_repository/data/si_rug.parquet"))

function to obtain reaction norm

get_si_rn <- function(df){
  ## read in the parameter sets
  par <- c(df$var1, df$var2, df$var3, df$var4)
  ## get cue range
  cue_range <- seq(df$low, df$high, by = df$by)
  ## nested function to convert parameter set into basis spline function
  rn <- par_to_df(par = par, cue_range = cue_range)
  
  # if parasite is sensing logged cue, we have to exponentiate it back so they are on the same scale! 
  rn2 <- rn
  if(stringr::str_detect(df$log, "log")){rn2$cue_range <- 10^(rn2$cue_range)}

  # append label to reaction norm, dyn, and rug
  rn2 <- data.frame(rn2, id = df$id)
  
  return(rn2)
}

run function to get reaction norms

# split dataframe of optimized rcues
si_opt.ls <- split(si_opt.df, seq(nrow(si_opt.df)))

# run function
si_rn <- lapply(si_opt.ls, get_si_rn)

# bind together
si_rn.df <- do.call(rbind, si_rn)
write_parquet(si_rn.df, here("code_repository/data/si_rn.parquet"))

preparing the dataset for plotting reaction norms and rugs

## we are only plotting relevent ranges of the reaction norm, meaning that these are the cue values actually "sensed" by the parasites in an infection. We can use the rug dataframe to get a rough estimate of that
si_rug_lim.df <- si_rug.df %>% 
  group_by(id)%>% 
  summarise(min_temp = min(value, na.rm = T)*0.9,
         max_temp = max(value, na.rm = T)*1.1) %>% ## calculate min and max for each cue
  left_join(si_opt.df, by = "id") %>% ## join with si_opt so we can get the cmmon cue
  ungroup() %>% 
  group_by(cue) %>% 
  summarise(min = min(min_temp), max = max(max_temp)) 

## process reaction norm data to restrict range and get label
si_rn.df_p <- si_rn.df %>% 
  left_join(select(si_opt.df, id, cue, log), by = "id") %>% ## get cue and log status from si_opt.df
  left_join(si_rug_lim.df, by = "cue") %>% ## via cue, get ranges we want to limit the rn to
  filter(cue_range <= max & cue_range >= min) %>% ## keep only cue values within range
  left_join(select(ez_label, id, cue_label), by = "id") # get labels


## refactor to reorder the order at which the cues are presented
si_rn.df_p$cue_label <- factor(si_rn.df_p$cue_label, 
                              levels = c("Asexual iRBC", "Sexual iRBC", "Total iRBC", "Gametocyte", "RBC"))

## split rug labels by none logged and logged
si_rug_cue.df <- si_rug.df %>% 
  distinct(id, value) %>% ## cut down on number of datapoints. For each id, keep only distinct points
  left_join(select(si_opt.df, id, cue, log), by = "id") %>% ## get cue and log status from si_opt.df
  left_join(select(ez_label, id, cue_label), by = "id") ## get cue label for plotting

## refactor rug
si_rug_cue.df$cue_label <- factor(si_rug_cue.df$cue_label, 
                              levels = c("Asexual iRBC", "Sexual iRBC", "Total iRBC", "Gametocyte", "RBC"))

### split
si_rug_cue.df_none <- si_rug_cue.df %>% filter(log == "none")
si_rug_cue.df_log <- si_rug_cue.df %>% filter(log == "log")

plot reaction norms and associated rug plots

## note that geom_point contains a set of scripts aimed at thinning the data points so they do not overcrowd!
fitness_rn.plt <- ggplot() +
  geom_line(data = si_rn.df_p, aes(x = cue_range, y = cr, color = log)) +
  geom_point(data = si_rn.df_p %>% 
  mutate(rel_cue = round(cue_range/(max-min)*100)) %>% 
    distinct(id, rel_cue, .keep_all = T) %>% 
    filter(rel_cue %% 10 ==1),
             aes(x = cue_range, y = cr, color = log, shape = log), size = 2) +
   geom_rug(data = si_rug_cue.df_none, aes(x = value), color = "#fc8d59", sides = "t", length = unit(0.1, "npc")) +
  geom_rug(data = si_rug_cue.df_log, aes(x = value), color = "#4575b4", sides = "b", length = unit(0.1, "npc")) +
  facet_wrap(~cue_label, scales = "free_x", ncol = 1) +
  labs(x = "Cue range", y = "Conversion rate", color = "Cue status", shape = "Cue status") +
  ylim(0, 1) +
  scale_color_manual(values=c( "#4575b4", "#fc8d59")) +
  scale_x_continuous(labels = function(x) format(x, scientific = T),
                     guide = guide_axis(check.overlap = TRUE)) + 
  theme_bw() +
  theme(axis.text.x = element_text(size = 8), legend.position = "bottom",
        
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

#———— Plotting fitness, dynamics, and rn together ————#

ggarrange(fitness_rank_cr.plt, fitness_rn.plt, widths = c(2.7, 1), align = "h", ncol = 2, labels = c("A", "B"))
ggsave(units = "px", dpi = 300, width = 2250, height = 1500, filename = here("code_repository/figures/fitness_rn.tiff"), bg = "white", scale = 1.25)

#=================================# # Dual cue fitness main figure #=================================# #————Relative ranking of dual cue vs single cue fitness———–# ## Prepare dual cue dataset

## For the dual cue models, we optimized each cue combinations in 2 ways, one with L-BFGS-B with 0.5X9 starting point and another with DEoptim + L-FGBS-B. For each cue combination, we are going to pick the strategy that gave us the higher fitness
dual_cue_f_final.df <- dual_cue_f_glb.df %>% 
  select(id, id_b, label, label_b, fitness_glb = fitness, 
         par1_glb = par1, par2_glb = par2, par3_glb = par3, par4_glb = par4, par5_glb = par5, par6_glb = par6,
         par7_glb = par7, par8_glb = par8, par9_glb = par9) %>% 
  left_join(
    select(dual_cue_f_lc.df,
         id, id_b, fitness_lc = fitness, 
         par1_lc = par1, par2_lc = par2, par3_lc = par3, par4_lc = par4, par5_lc = par5, par6_lc = par6,
         par7_lc = par7, par8_lc = par8, par9_lc = par9), by = c("id", "id_b")
  ) %>% ## rename columns and join global and local optimization fitness dataframes
  mutate(
    fitness = ifelse(fitness_glb > fitness_lc, fitness_glb, fitness_lc),
    par1 = ifelse(fitness_glb > fitness_lc, par1_glb, par1_lc),
    par2 = ifelse(fitness_glb > fitness_lc, par2_glb, par2_lc),
    par3 = ifelse(fitness_glb > fitness_lc, par3_glb, par3_lc),
    par4 = ifelse(fitness_glb > fitness_lc, par4_glb, par4_lc),
    par5 = ifelse(fitness_glb > fitness_lc, par5_glb, par5_lc),
    par6 = ifelse(fitness_glb > fitness_lc, par6_glb, par6_lc),
    par7 = ifelse(fitness_glb > fitness_lc, par7_glb, par7_lc),
    par8 = ifelse(fitness_glb > fitness_lc, par8_glb, par8_lc),
    par9 = ifelse(fitness_glb > fitness_lc, par9_glb, par9_lc)
  ) ## the columns are assigned based on which strategy produced the highest fitness

## write csv
write.csv(dual_cue_f_final.df, here("code_repository/data/dual_cue_fitness_final.csv"))

Combine single cue and dual cue dataset

dual_cue_f_final.df <- read.csv(here("code_repository/data/dual_cue_fitness_final.csv"))

dual_si_fitness.df <- dual_cue_f_final.df %>% 
  select(id, id_b, label, label_b, fitness_dual = fitness) %>% 
  left_join(select(si_opt.df, id, fitness_si = fitness_20), by = "id") %>% ## get fitness of single cue model based on first cue
  left_join(select(si_opt.df, id_b = id, fitness_si_b = fitness_20), by = "id_b") %>%  ## get fitness of single cue model based on second cue
  mutate(fitness_si_final = ifelse(fitness_si > fitness_si_b, fitness_si, fitness_si_b)/9.883602,
         label_comb = paste(label, "&", label_b), ## select the highest single cue model fitness
         fitness_dual_norm = fitness_dual/9.883602)## normalize by time fitness

plot fitness rank

#————Time series conversion rate of dual cue models———–# Note that our dual cue models take 9 parameters. To ensure that any difference between dual and single cue models is due to the inclusion of additional cues and NOT higher spline flexibility,we performed L-BFGS-B (local) optimization of single cue models with df = 9 to control for the spline flexibility.

dynamics simulation of high parameter cues

## best dual cue model: I log and R log
Rlog_Ilog.cr <- chabaudi_si_clean(
  parameters_cr = c(4.446192033,    10.97518275,    1.38762817, 23.3059254, -3.452052371,   -18.0070692,    39.66614226,    -3.545193141,   18.78350799),
  immunity = "tsukushi",
  parameters = parameters_tsukushi,
  time_range = seq(0, 20, by = 1e-3),
  cue_range =  seq(6, 7, by = 1/500),
  cue_range_b = seq(0, log10(6*(10^6)), by = (log10(6*(10^6)))/500),
  cue = "R",
  cue_b = "I",
  log_cue = "log10",
  log_cue_b = "log10",
  solver = "vode",
  dyn = T
)

## when time is used as a cue (9 parameters). chabaudi_si_clean_high is simply a variation of chabaudi_si_clean that makes a more flexible spline!
time_high.cr <- chabaudi_si_clean_high(
  parameters_cr = c(9.154314,  -7.570829, -22.506638 ,  3.382405 ,-13.453519 ,-17.011485  , 3.678181, -12.851895 ,-26.115158),
  immunity = "tsukushi",
  parameters = parameters_tsukushi,
  time_range = seq(0, 20, by = 1e-3),
  cue_range =  seq(0, 20, by = 1e-3),
  cue = "t",
  solver = "vode",
  dyn = T)

## when asexual iRBC is used as a cue (high flexibility)
I_high.cr <- chabaudi_si_clean_high(
  parameters_cr = c(1.296675,  3.544034 , 4.907484,  2.174249, -3.238309 ,-5.181614 ,-1.645072 , 1.834302 , 1.581011),
  immunity = "tsukushi",
  parameters = parameters_tsukushi,
  time_range = seq(0, 20, by = 1e-3),
  cue_range =  seq(0, log10(6*(10^6)), by = (log10(6*(10^6)))/5000),
  cue = "I",
  log_cue = "log10",
  solver = "vode",
  dyn = T)

## when RBC is used as cue (high flexibility)
R_high.cr <- chabaudi_si_clean_high(
  parameters_cr = c(5.0340348 ,  0.5846168 ,  0.3749648 ,  0.6842673  , 2.4748107 , 10.9036034 , 16.8246316, -24.8690971 ,1.8007238),
  immunity = "tsukushi",
  parameters = parameters_tsukushi,
  time_range = seq(0, 20, by = 1e-3),
  cue_range =  seq(log10(10^6), log10(10^7), by = (log10(10^7)-log10(10^6))/5000),
  cue = "R",
  log_cue = "log10",
  solver = "vode",
  dyn = T)

plot

dual_selected_cr.pl <- ggplot() +
  geom_line(data = dual_selected_cr.df, aes(color = label_new, x = time, y = value), size = 1) +
  geom_point(data = dual_selected_cr.df %>% filter(time%%1 == 0), aes(color = label_new, x = time, y = value, shape = label_new), size = 3) +
  labs(x = "Time (days)", y = "Conversion rate", color = "Cue(s)", shape = "Cue(s)") +
  xlim(0, 20) +
  scale_color_manual(values = c("#fc8d59","#fdcb44","black", "#4575b4")) +
  theme_classic() +
  theme(legend.position="right",
        plot.margin = margin(t = 40, r = 0, b = 0, l = 0, unit = "pt")) +
  guides(color = guide_legend(nrow = 4, byrow = TRUE))

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

#——– Reaction norm heatmap of R log10 + I log10 ————# # Process data

# make heatmap data.frame
Rlog_Ilog.hm <- par_to_hm_te(par = c(4.446192033,   10.97518275,    1.38762817, 23.3059254, -3.452052371,   -18.0070692,    39.66614226,    -3.545193141,   18.78350799),
             cue_range = seq(6, 7, length.out = 500),
             cue_range_b = seq(0,   6.77815125, length.out = 500))

# process dynamics
Rlog_Ilog.dyn <- Rlog_Ilog.cr %>% 
  tidyr::pivot_wider(names_from = variable, values_from = value) %>% 
  mutate(log_R = log10(R),
         log_I = log10(I))

plot

Rlog_Ilog_rn.pl <- ggplot() +
  geom_raster(data = Rlog_Ilog.hm, aes(x = cue_range_b, y = cue_range, fill = cr)) +
  scale_fill_viridis_c() +
  geom_path(data = Rlog_Ilog.dyn, aes(x = log_I, y = log_R), color = "white", arrow = arrow(angle = 30, length = unit(0.1, "inches"))) +
  geom_point(data = Rlog_Ilog.dyn %>% filter(row_number() %% 1000 == 1 & time <= 20), aes(x = log_I, y = log_R), color = "white") +
  xlim(0.99*min(hablar::s(Rlog_Ilog.dyn$log_I), na.rm = T), 1.01* max(hablar::s(Rlog_Ilog.dyn$log_I), na.rm = T)) +
  ylim(0.99*min(hablar::s(Rlog_Ilog.dyn$log_R), na.rm = T),1.01* max(hablar::s(Rlog_Ilog.dyn$log_R), na.rm = T)) +
  labs(y = "RBC log10", x = "Asexual iRBC log10", fill = "Conversion\nrate") +
  theme_classic() +
  theme(legend.position = "right")

#———– Plot together ————#

#=================================# # Dual cue conversion rate supplementary figure #=================================# ## Function to simulate dual cue dynamics

dual_cue_dyn <- function(df){
  ## process cues
  cue <- df$cue
  cue_b <- df$cue_b
  
  ## process log
  log <- ifelse(str_detect(df$id, "log"), "log10", "none")
  log_b <- ifelse(str_detect(df$id_b, "log"), "log10", "none")
  
  # process cue_range. ensure that both cue ranges are of the same length
  cue_range <- seq(df$low, df$high, length.out = 500)
  cue_range_b <- seq(df$low_b, df$high_b, length.out = 500)
  
  # get parameter set
  par <- c(df$par1, df$par2, df$par3, df$par4, df$par5, df$par6, df$par7, df$par8, df$par9)
  
  # simulate dynamics
  dyn <- chabaudi_si_clean(
    parameters_cr = par,
    immunity = "tsukushi",
    parameters = parameters_tsukushi,
    time_range = seq(0, 20, 0.01),
    cue = cue,
    cue_b = cue_b,
    cue_range = cue_range,
    cue_range_b = cue_range_b,
    log_cue = log,
    log_cue_b = log_b,
    solver = "vode",
    gam = "te",
    dyn = T)
  
  # return 
  dyn2 <- cbind(id = df$id, id_b = df$id_b, 
                label = df$label, label_b = df$label_b,
                cue = cue, cue_b = cue_b, dyn)
  
  write_parquet(dyn2, here(paste0("code_repository/data/dual_cue_dyn/", df$id, "_", df$id_b, ".parquet")))
}

Get dynamics of dual cue models

## For the dual cue fitness dataframe, add the cue range for each individual cue
dual_cue_f_final.df_p <- dual_cue_f_final.df %>% 
  left_join(select(cue_range_si_alt.df, id, cue, low, high), by = "id") %>% 
  left_join(select(cue_range_si_alt.df, id_b = id, cue_b = cue, low_b = low, high_b = high), by = "id_b")

## Split dataframes
dual_cue_f_final.ls <- split(dual_cue_f_final.df_p, seq(nrow(dual_cue_f_final.df_p)))

## Run function
mclapply(dual_cue_f_final.ls, dual_cue_dyn, mc.cores = 6)

## Concat all files
dual_cue_dyn.ls <- list.files(path = here("code_repository/data/dual_cue_dyn"), pattern = "*.parquet", full.names = T)
dual_cue_dyn.df <- do.call(rbind, lapply(dual_cue_dyn.ls, read_parquet))
write_parquet(dual_cue_dyn.df, here("code_repository/data/dual_cue_dyn.parquet"))

get 30 days dynamic (for plotting purposes)

## Run function
mclapply(dual_cue_f_final.ls, dual_cue_dyn, mc.cores = 6)

## Concat all files
dual_cue_dyn_30.ls <- list.files(path = here("code_repository/data/dual_cue_dyn_30"), pattern = "*.parquet", full.names = T)
dual_cue_dyn_30.df <- do.call(rbind, lapply(dual_cue_dyn_30.ls, read_parquet))
write_parquet(dual_cue_dyn_30.df, here("code_repository/data/dual_cue_dyn_30.parquet"))

Preparing dataset for plotting

## filter out only conversion rate and attach fitness values
dual_cue_cr.df <- dual_cue_dyn.df %>% 
  filter(variable == "cr") %>% 
  left_join(select(dual_cue_f_final.df, id, id_b, fitness), by = c("id", "id_b")) %>% 
  mutate(label_comb = paste(label, "&", label_b))

## Sanity check that the dynamics produced the same fitness as the optimized values. Yes!
dual_cue_dyn.df %>% 
  filter(variable == "tau_cum") %>% 
  filter(time == 20) %>% 
  distinct(id, id_b, value) %>% 
  left_join(select(dual_cue_f_final.df, id, id_b, fitness), by = c("id", "id_b")) %>% 
  mutate(diff = value-fitness)

Plot

ggsave(here("code_repository/figures/dual_cue_cr.tiff"), units = "px", width = 2000, height = 1500, dpi=300,  bg = "white")

Warning: Removed 4080 rows containing missing values (`geom_raster()`).

#=================================# # Experimental disease maps of P. chabaudi #=================================# ## Import in data

# import in https://academic.oup.com/emph/article/2018/1/127/5045871?login=true
## (2018 published in EMPH)
emph_2018 <- readxl::read_xls(here("code_repository/experimental_data/Huijben_2018_EMPH.xls"), sheet = 1)

# import in https://onlinelibrary.wiley.com/doi/10.1111/j.1558-5646.2010.01068.x
## (2010 published in Evolution)
evo_2010 <- readxl::read_xls(here("code_repository/experimental_data/Huijben_2010_evolution.xls"), sheet = 1)

# import in https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1003578#:~:text=The%20philosophy%20is%20that%20aggressive,longer%20feel%20sick%20%5B13%5D.
## (2013 in PLoS pathogen)
plos_2013_1 <- readxl::read_xlsx(here("code_repository/experimental_data/Huijben_2013_PLoS.xlsx"), sheet = 2)

plos_2013_2 <- readxl::read_xlsx(here("code_repository/experimental_data/Huijben_2013_PLoS.xlsx"), sheet = 3)

# import in https://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2011.02369.x
## (2011 in Journal of Evolutionary Biology)
eseb_2011 <- readxl::read_xls(here("code_repository/experimental_data/Huijben_2011_eseb.xls"), sheet = 1)

# import in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939351/
## (2011 in Journal of American naturalist). Note private dataset so not provided in the supplementary!
amna_2011 <- readxl::read_xls(here("experimental_data/Pollitt_2011_naturalist.xls"), sheet = 1)

Clean data

## for EMPH 2018 study, include only infection series without drugs were R-inoculum is administered by itself, which includes 6, 7, 8, 9, 10. Box 6 has a starting inoculum number of 10^6, which is most similar to other studies. Filtering between day 3-21 because those are the days where we have single day data.
emph_2018_ss.df <- emph_2018 %>% 
  filter(Box %in% seq(6, 10) &
         dplyr::between(Day, 3, 21)) %>% 
  mutate(dose = case_when(
    Box == 6 ~ 10^6,
    Box == 7 ~ 10^5,
    Box == 8 ~ 10^3,
    Box == 9 | Box == 10 ~ 10^1
  )) %>% 
  mutate(strain = "As6p",
         study = "emph2018",
         study_strain = paste0(strain, study),
         id = paste0(study, strain, Box, Mouse, 1),
         RBC = RBC * (10^6)) %>%
  select(day = Day,
         mouse = Mouse, 
         RBC, 
         asex = Rasex,
         gam = Rgam,
         dose,
         strain,
         study,
         study_strain,
         id)

## for 2011 eseb, only day 3-17 data are analyzed because those are the days where gametocyte data are available
eseb_2011_ss.df <- eseb_2011 %>% 
  filter(Clones == "R" & between(Day, 3, 17) &
           Drugs == "N") %>% 
  mutate(dose = 10^6,
         strain = "As8p",
         study = "eseb2011",
         study_strain = paste0(strain, study),
         RBC = RBC*(10^6),
         id = paste0(study, strain, Box, Mouse, 2)) %>% 
  select(day = Day,
         mouse = Mouse,
         RBC,
         asex = R.asex,
         gam = R.gam,
         dose,
         strain,
         study,
         study_strain,
         id)

## for evolution_2010, single infection data for both resistant and susceptible clones are available without drug treatment
evolution_2010_ss.df <- evo_2010 %>% 
  filter(Clone == "R" | Clone == "S") %>%
  filter(between(Day, 3, 21) &
           Drugs == "nodrugs") %>% 
  mutate(asex = R.asex + S.asex,
         gam = R.gam + S.gam,
         dose = 10^6,
         RBC = RBC*(10^6),
         study = "evol2011",
         strain = ifelse(Clone == "R", "As12", "AJ51"),
         study_strain = paste0(strain, "_", study),
         id = paste0(study, strain, Box, Mouse, 3)) %>% 
  select(day = Day,
         mouse = Mouse,
         RBC,
         asex,
         gam,
         dose,
         strain,
         study,
         study_strain,
         id)

## for amnat 2011, get single infection data. Filter out any mice that have missing data. Set negative asexuasl data ot 0
amna_2011_ss.df <-  amna_2011 %>% 
  filter(treat %in% c("AJ", "AS", "ER", "CR", "CW", "DK")) %>% 
  mutate(asex = tot.para - tot.gcyte,
         gam = tot.gcyte,
         dose = 10^6,
         study = "amna_2011",
         RBC = rbc/(10^6),
         study_strain = paste0(treat, "_", study),
         id = paste0(study, treat, div, mouse, 4)) %>% 
  mutate(asex = ifelse(asex < 0, 0, asex)) # sometimes total parasite is less than gametocyte so need to correct for this

### check for NA by groups
amna_na.id <- amna_2011_ss.df %>% 
  filter_at(vars(asex, gam, RBC), all_vars(is.na(.))) %>% 
  distinct(id) %>% 
  select(id)

amna_2011_ss.df2 <- amna_2011_ss.df %>% 
  filter(!(id %in% amna_na.id$id)) %>% 
  select(day,
         mouse,
         RBC,
         asex,
         gam,
         dose,
         strain = treat,
         study,
         study_strain,
         id)

## rbind
exp_ss.df <- rbind(emph_2018_ss.df, eseb_2011_ss.df, evolution_2010_ss.df, amna_2011_ss.df2)

## write
write.csv(exp_ss.df, here("code_repository/data/experimental_data.csv"))

Prepare dataset for plotting

names(exp_ss.df) <- c("X", "Day", "Mouse", "RBC", "iRBC", "Gametocyte", "Dose", "Strain", "Study", "Study_strain", "id")

## prepare a list of variable combinations we want to plot
exp_var.comb <- tidyr::expand_grid(x = c("RBC", "iRBC", "Gametocyte"),
                   y = c("RBC", "iRBC", "Gametocyte")) %>% ## get all pairwise combinations of variables
  filter(x != y) %>% ## remove incidences where the 2 variables are the same
  mutate(tmp = paste0(pmin(x, y), pmax(x, y))) %>% ## eliminate same variable but different order
  slice_head(n = 1, by = tmp) %>% 
  select(-tmp) 

List-wise plotting

## x and y axis are not logged!
exp_xy.pl_ls <- map2(exp_var.comb$x, exp_var.comb$y, ~ {
  x_col <- .x
  y_col <- .y
  
  ggplot(exp_ss.df, aes_string(x = x_col, y = y_col)) +
        geom_path(aes(colour = Day, group = id), arrow = arrow(type = "closed", angle = 10, length = unit(0, "inches"))) +
        theme_classic() + 
        scale_color_viridis_c(option = "A", limits = c(3, 21)) +
        labs(color = "Days post-infection")  +
        scale_y_continuous(labels = function(x) format(x, scientific = TRUE)) +
        scale_x_continuous(labels = label_scientific(digits = 1))
}
)

## x-axis is logged
exp_xlogy.pl_ls <- map2(exp_var.comb$x, exp_var.comb$y, ~ {
  x_col <- .x
  y_col <- .y
  
  ggplot(exp_ss.df, aes_string(x = sprintf("log10(%s)", x_col), y = y_col)) +
        geom_path(aes(colour = Day, group = id), arrow = arrow(type = "closed", angle = 10, length = unit(0, "inches"))) +
        theme_classic() + 
        scale_color_viridis_c(option = "A", limits = c(3, 21)) +
        labs(color = "Days post-infection", x = paste(x_col, "log"))  +
        scale_y_continuous(labels = function(x) format(x, scientific = TRUE))
}
)

## y-axis logged
exp_xylog.pl_ls <- map2(exp_var.comb$x, exp_var.comb$y, ~ {
  x_col <- .x
  y_col <- .y
  
  ggplot(exp_ss.df, aes_string(x = x_col, y = sprintf("log10(%s)", y_col))) +
        geom_path(aes(colour = Day, group = id), arrow = arrow(type = "closed", angle = 10, length = unit(0, "inches"))) +
        theme_classic() + 
        scale_color_viridis_c(option = "A", limits = c(3, 21)) +
        labs(color = "Days post-infection", y = paste(y_col, "log"))   +
        scale_x_continuous(labels = label_scientific(digits = 1))
}
)

## both x and y axis is logged
exp_xlogylog.pl_ls <- map2(exp_var.comb$x, exp_var.comb$y, ~ {
  x_col <- .x
  y_col <- .y
  
  ggplot(exp_ss.df, aes_string(x = sprintf("log10(%s)", x_col), y = sprintf("log10(%s)", y_col))) +
        geom_path(aes(colour = Day, group = id), arrow = arrow(type = "closed", angle = 10, length = unit(0, "inches"))) +
        theme_classic() + 
        scale_color_viridis_c(option = "A", limits = c(3, 21)) +
        labs(color = "Days post-infection", x = paste(x_col, "log"), y = paste(y_col, "log")) 
}
)

## plot together
ggarrange(plotlist = c(exp_xy.pl_ls, exp_xlogy.pl_ls, exp_xylog.pl_ls, exp_xlogylog.pl_ls), 
          common.legend = T, align = "hv")
ggsave(here("code_repository/figures/exp_disease-curve.tiff"), units = "px", width = 2250, height = 1500, scale = 1.4, dpi=300,  bg = "white")

#=================================# # Simulated disease curve graph #=================================# ## Function to obtain dynamics data based on the dual cue input

get_dual_rn <- function(df){
  ## assign the two cues
  cue <- unique(df$cue)
  cue_b <- unique(df$cue_b)
  
  ## assign log status
  log <- ifelse(str_detect(unique(df$id), "log"), "log", "none")
  log_b <- ifelse(str_detect(unique(df$id_b), "log"), "log", "none")
  
  ## assign which cues are going to be logged
  if(log == "log" & log_b == "none"){logged_cue <- cue}
  if(log == "none" & log_b == "log"){logged_cue <- cue_b}
  if(log == "log" & log_b == "log"){logged_cue <- c(cue, cue_b)}
  if(log == "none" & log_b == "none"){logged_cue <- c()}

  ## keep variables that corresponds to the cue used
  ### for dataframes that does not involve combined variables such as I+Ig
  if(isTRUE(str_detect(cue, "\\+", negate = T)) & isTRUE(str_detect(cue_b, "\\+", negate = T))){
    df_f <- df %>% 
      filter(variable %in% c(cue, cue_b, "cr")) %>% 
      mutate(value = case_when(
        variable %in% logged_cue ~ log10(value),
        TRUE ~ value
      )) %>% ## log transform values only when they match with the logged cue list
      filter(value >= 0) ## filter out values <0, these happen due to stiffness of models but are not relevant
         
  } else{
    ### assign both cues to a list
    cue_ls <- c(cue, cue_b)
    ### pick the cue that has the "+" sign
    combined_cue <- cue_ls[grepl("\\+", cue_ls)]
    non_combined_cue <- cue_ls[!grepl("\\+", cue_ls)] ### this is the none combined cue
    ### unlist the cues
    cue_unlist <- unlist(str_split(combined_cue, "\\+"))
    
    ### get filtered dataset containing only non-combine cue
    df_f1 <- df %>% 
      filter(variable %in% c(non_combined_cue, "cr"))
    
    ### get filtered dataset containing combined cue. These will be summed up and bound back to the previous
    df_f2 <- df %>% 
      filter(variable %in% cue_unlist) %>% ## keep only variables that we will combine
      group_by(time) %>% ## for each time point, group the variables
      mutate(value = sum(value, na.rm = T),
             variable = combined_cue) %>% ## recalculate the value as sum of the values and reassign variable!
      distinct(time, .keep_all = T) ## note that because we are mutating we must dedeuplicate the records
    
    #### combine the two and log transform if necessary
    df_f <- rbind(df_f1, df_f2) %>% 
      mutate(value = ifelse(variable %in% logged_cue, log10(value), value)) %>% ## log transform values only when they match with the logged cue list
      filter(value >= 0) 
  }
  
  ## Convert dataframes wider such that the different variables have their own columns
  df_fp <- df_f %>% 
    mutate(variable_id = ifelse(variable == cue, paste0(variable, "_", log), paste0(variable, "_", log_b))) %>%  ### assign a unique variable id that could later be used to assign labels
    left_join(select(ez_label, id, long_label), by = c("variable_id" = "id")) %>% 
    mutate(long_label = ifelse(variable == "cr", "cr", long_label)) %>% ## manually add cr
    mutate(long_label = gsub(" ", "_", long_label)) %>%  ## convert spaces to _ for plotting
    pivot_wider(names_from = long_label, values_from = value, id_cols = c(time, id, id_b)) %>% 
    filter(time >= 1) %>% ## filter out day 0->1 because all cr = 0 before that
    arrange(time) ## this is needed to prevent geom_path from joining the first and last data point
  
  ## assign NAs (meaning no stuff is produced yet to 0)
  df_fp[is.na(df_fp)] <- 0

  return(df_fp)
}

Run function to get a curated dataset containing only relevant

## split dual dynamics dataframe into list grouped by the dual cues
dual_cue_dyn.ls <- dual_cue_dyn.df %>% group_split(id, id_b)

## run function across list
dual_cue_rn.ls <- mclapply(dual_cue_dyn.ls, get_dual_rn, mc.cores = 6)

## sanity checks that we are actually summing iRBCs. Rings out
max(dual_cue_rn.ls[[2]]$Total_iRBC)
max(dual_cue_rn.ls[[4]]$Asexual_iRBC)
max(dual_cue_rn.ls[[6]]$Sexual_iRBC)

max(dual_cue_rn.ls[[10]]$Total_iRBC)
max(dual_cue_rn.ls[[12]]$Asexual_iRBC)
max(dual_cue_rn.ls[[14]]$Sexual_iRBC)

plot

## list apply all dataframes
cue_cue_rn_pl.ls <- lapply(dual_cue_rn.ls,
       function(x){
          ## get names of columns used in the x and y axis
         axis_cols <- setdiff(names(x), c("time", "id", "id_b", "cr"))
         
         ## ggplot
         ggplot() +
           geom_path(data = x, aes_string(x = axis_cols[[1]], y = axis_cols[[2]], color = "cr"),
                     arrow = arrow(length = unit(c(rep(0, nrow(x) - 2), 0.25), "inches")),
                     size = 1.5) +
           geom_point(data = x %>% filter(time %% 1 == 0), 
                      aes_string(x = axis_cols[[1]], y = axis_cols[[2]]), size = 1.5, shape = 1) +
           theme_classic() +
           scale_color_viridis_c(limits = c(0, 1)) +
           labs(color = "Conversion rate", x = gsub("_", " ", axis_cols[[1]]), y = gsub("_", " ", axis_cols[[2]])) +
           scale_x_continuous(labels = label_scientific(digits = 2)) +
           scale_y_continuous(labels = label_scientific(digits = 2))
       })

## arrange together
cue_cue_rn.pl <- ggarrange(plotlist = cue_cue_rn_pl.ls, ncol = 5, nrow = 8, common.legend = T, align = "hv") 
ggsave(here("code_repository/figures/sim_disease-curve.tiff"), units = "px", width = 2250, height = 2500, scale = 2.2, dpi=300,  bg = "white")

#=================================# # Curating list of selected exo and simulated # disease curves (main figure) #=================================# # plot

# main figure
ggarrange(
   cue_cue_rn_pl.ls[[31]] +
    scale_x_continuous(labels = scales::number_format(accuracy = 0.1)) +
    scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
    theme(legend.position = "none"), 
          
   exp_xlogylog.pl_ls[[1]] + theme(legend.position = "none"),

   cue_cue_rn_pl.ls[[35]] + 
     scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
     theme(legend.position = "none",
           axis.text.x=element_text(size=6.5)), 
   
   exp_xylog.pl_ls[[1]] + 
     scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
     theme(legend.position = "none",
           axis.text.x=element_text(size=6.5)),
   
   cue_cue_rn_pl.ls[[30]] + 
     scale_x_continuous(labels = scales::number_format(accuracy = 0.1)) +
     theme(legend.position = "none"), 
   
   exp_xlogy.pl_ls[[1]] + theme(legend.position = "none"), 
   
   cue_cue_rn_pl.ls[[15]] + 
     scale_x_continuous(labels = scales::number_format(accuracy = 0.1)) +
     theme(legend.position = "none"), 
   
   exp_xlogy.pl_ls[[2]] + theme(legend.position = "none"),
   
   align = "hv", ncol = 4, nrow = 2
)

Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

ggsave(here("code_repository/figures/sim_exp_disease_curve_main.tiff"), units = "px", width = 2250, height = 1000, scale = 1.25, dpi=300,  bg = "white")

# get legend separately
ggarrange(
   cue_cue_rn_pl.ls[[31]] +
    scale_x_continuous(labels = scales::number_format(accuracy = 0.1)) +
    scale_y_continuous(labels = scales::number_format(accuracy = 1)) +
    theme(legend.position = "top"), 
          
   exp_xlogylog.pl_ls[[1]] + theme(legend.position = "top"))

Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

ggsave(here("code_repository/figures/sim_exp_disease_curve_legend.tiff"), units = "px", width = 2250, height = 500, scale = 1.25, dpi=300,  bg = "white")

#=================================# # MC simulation of single cue and dual cue infection #=================================# #———— Impact of all parameter variation on fitness ————# ## Append all fitness data and sanity checks

process data for plotting fitness

## normalize fitness and take only first 1000 measurement (this is done given that subsequent simulations only include 1000 reps!)
mc_all_fitness.df_p <- mc_all_fitness.df %>% 
  filter(iter %in% seq(1,1000,1)) %>% 
  mutate(fitness_norm = max_fitness/9.883602)

## get the reference fitness (default parameter variation), which is where iter = 1
mc_all_fitness_ref.df <- mc_all_fitness.df_p %>% filter(iter == 1)

## get the rest of the data points (excluding iter == 1) and calculate median and mean
mc_all_fitness_rand.df <- mc_all_fitness.df_p %>% 
  filter(iter != 1) 

## get mean and mode in a separate df
mc_all_fitness_sum.df <- mc_all_fitness_rand.df %>% 
  group_by(short_label) %>% 
  summarize(mean = mean(fitness_norm),
            median = median(fitness_norm),
            geom_mean = exp(mean(log(fitness_norm)))) 

plot fitness variation

#———— Impact of individual parameter variation on fitness ————# ## combine all single parameter variation files

## list of file paths linked to the single parameter files
mc_single.ls <- list.files(path = here("code_repository/data/mc_single_fitness"), pattern = "*.csv", full.names = T)

## filter out R log and I log data. these will be attached later
mc_single_sc.ls <- mc_single.ls[!grepl("-", basename(mc_single.ls))]
mc_single_dc.ls <- mc_single.ls[grepl("-", basename(mc_single.ls))]

## check number of files
length(mc_single_sc.ls)
length(mc_single_dc.ls)

## read and combine
mc_single_sc_fitness.df <- do.call(rbind, mclapply(mc_single_sc.ls, function(x) read.csv(x), mc.cores = 6))
mc_single_dc_fitness.df <- do.call(rbind, mclapply(mc_single_dc.ls, function(x) read.csv(x), mc.cores = 6))

# look at # of reps
mc_single_sc_fitness.df %>% 
  group_by(cue, log) %>% 
  tally()

mc_single_dc_fitness.df %>% 
  group_by(cue, log, cue_b, log_b) %>% 
  tally()

## combine
mc_single_fitness.df <- mc_single_dc_fitness.df %>% 
  mutate(iter = id,
         id = paste0(cue, "_", log, "-", cue_b,"_", log_b)) %>%
  select(-c(cue, cue_b, log, log_b)) %>% 
  rbind(
    mc_single_sc_fitness.df %>% 
      mutate(iter = id,
             id = ifelse(cue != "t", paste0(cue, "_", gsub("log10", "log", log)), "time")
             ) %>% 
      select(-c(cue, log))
    ) %>% 
  left_join(ez_label, by = "id") 


## write
write_parquet(mc_single_fitness.df, here("code_repository/data/mc_single_fitness.parquet"))

## sanity check. iter = 1 is where all parameters are default. Should produce the same fitness values!
mc_single_fitness.df %>% filter(iter == 1)

## sanity check. We should have the same values for iter 1 across all and single parameter datasets. 
mc_single_fitness.df %>% filter(iter == 1)

process data for plotting

## calculate summary statistics. this includes median, credible interval (contains 89% data points calculated via Highest Density Interval, which is better for skewed data)
mc_single_fitness.sum <- mc_single_fitness.long_p %>% 
  group_by(short_label, parameter) %>% 
  summarise(ci_lower = ci(rel_diff_single, method = "HDI", ci = 0.89)[[2]],
            ci_higher = ci(rel_diff_single, method = "HDI", ci = 0.89)[[3]],
            quantile_low = quantile(rel_diff_single, 0.025),
            quantile_high = quantile(rel_diff_single, 0.975),
            median = median(rel_diff_single),
            mean = mean(rel_diff_single)) %>% 
  mutate(parameter_label = case_when( ## recode parameter values
    parameter == "rho" ~ "RBC replenishment (ρ)",
    parameter == "phin" ~ "Half-life indis (ϕn)",
    parameter == "phiw" ~ "Half-life targeted (ϕw)",
    parameter == "psin" ~ "Activation indis (ψn)",
    parameter == "psiw" ~ "Activation targeted (ψw)",
    parameter == "beta" ~ "Burst size (β)",
  ))

`summarise()` has grouped output by 'short_label'. You can override using the `.groups` argument.

plotting 89% credible interval. By visual inspection of the distribution, ci represented the distribution (more honestly) than quantile, even when quantile seems to display larger differences of logging cues.

 ggplot() +
  geom_violin(data = mc_single_fitness.long_p_f, aes(x = rel_diff_single, y = short_label,
                                                 ), fill = "grey", color = "grey") + ## 89% credible interval
  geom_point(data = mc_single_fitness.sum, aes(x = median, y = short_label)) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "#785EF0") +
  geom_vline(xintercept = 1, linetype = "dashed", color = "#785EF0") +
  facet_wrap(~parameter_label) +
  labs(x = "Relative fitness pertubation", y = "Cues") +
  theme_bw() +
  scale_y_discrete(limits=rev) + ## reverse ordering of y-axis so that cues are displayed correctly
  theme(legend.position = "none",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
  )

Error in fortify(data) : object 'mc_single_fitness.long_p_f' not found

#——— arrange plots together ———#

ggarrange(mc_all_fitness.pl, mc_partition.pl, align = "h", widths = c(1.3, 2), labels = c("A", "B"))
ggsave(units = "px", dpi = 300, width = 2250, height = 1500, filename = here("code_repository/figures/mc_fitness_partition.tiff"), bg = "white", scale = 1.2)

#==========================================# # Supplementary figure on MC posterior parameter distribution #==========================================# ## Process data

plot

## plot together
ggarrange(posterior_1.pl, posterior_2.pl, ncol = 1, align = "hv")

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#==========================================# # Rank plot of parasite fitness with only one parameter varying #==========================================# ## Process data

## simpler version of long table containing fitness of parasites when various parameter is pertubed
mc_single_fitness.long2 <- mc_single_fitness.df %>% 
  filter(iter %in% seq(0,1000,1)) %>% ## filter out iter = 1, which does not have variation
  distinct(id, iter, .keep_all = T) %>% ## ensure there are not replicates
  filter(iter != 1) %>% 
  dplyr::select(id, fitness_rho, fitness_beta, fitness_psin, fitness_psiw, fitness_phin, fitness_phiw, iter) %>% # keep only fitness and associated labels
  tidyr::pivot_longer(-c("id", "iter")) %>% ## make long
  mutate(parameter = gsub("fitness_", "", name)) %>% 
  mutate(
         parameter_label = case_when( ## recode parameter values
    parameter == "rho" ~ "RBC replenishment (ρ)",
    parameter == "phin" ~ "Half-life indis (ϕn)",
    parameter == "phiw" ~ "Half-life targeted (ϕw)",
    parameter == "psin" ~ "Activation indis (ψn)",
    parameter == "psiw" ~ "Activation targeted (ψw)",
    parameter == "beta" ~ "Burst size (β)",
  ),
  fitness_norm = value/9.883602) %>%  ## normalize fitness value
  left_join(ez_label, by = c("id")) %>% 
  left_join(
    rbind(
    select(si_opt.df, id, fitness_det = fitness_20) ,## get deterministic fitness (single cue) 
    dual_cue_f_final.df %>% mutate(id = paste0(gsub("log", "log10", id), "-", gsub("log", "log10", id_b))) %>% select(id, fitness_det = fitness) ## get deterministic fitness (dual cue)
    ),
    by = "id") %>% 
  mutate(fitness_det_norm = fitness_det/9.883602) ## normalize deterministic fitness

mc_single_fitness.long2 %>% distinct(id, fitness_det)

## reorder parameter
mc_single_fitness.long2$parameter_label <- factor(mc_single_fitness.long2$parameter_label,
                                                     c("Burst size (β)",
                                                       "RBC replenishment (ρ)",
                                                       "Half-life indis (ϕn)",
                                                       "Half-life targeted (ϕw)",
                                                       "Activation indis (ψn)",
                                                       "Activation targeted (ψw)"))

## get median fitness values (normalized)
mc_single_fitness.sum2 <- mc_single_fitness.long2 %>% 
  group_by(short_label, id, parameter_label) %>% 
  summarize(median = median(fitness_norm),
            mean = mean(fitness_norm),
            geo_mean = exp(mean(log(fitness_norm))),
            fitness_det_norm = unique(fitness_det_norm)) %>% 
  left_join(select(mc_all_fitness_sum.df, short_label, geo_mean_all = geom_mean), by = "short_label") ## get geometric mean of when all parameter are varied

# left join geometric fitness to long df for ranking purposes
mc_single_fitness.long2 <- mc_single_fitness.long2 %>% 
  left_join(select(mc_single_fitness.sum2, id,parameter_label, median, mean, geo_mean), by = c("id", "parameter_label", "short_label")) 

plot

note that it is quite a hassel to reorder cues when facetting. Hence, I will just plot out all 6 plots individually

#==================================# # Understanding the mechanism behind robustness #==================================#

mclapply(posterior.ls[1:5], function(x){
  mc_single(par = c(0.1135384,  -39.0585154, 1941.9082813,  119.8228980),
                    cue = "I",
                    cue_range = seq(0, 6*(10^6), by = (6*(10^6))/5000),
                    log= "none",
                   rand_df = x)},
  mc.cores = 6)

$`1`
NULL

$`2`
NULL

$`3`
NULL

$`4`
NULL

$`5`
NULL

#=====================================# # Impact of cue perception on virulence #====================================# #——— Divid dynamics data into high and low performing —————# # Single cue

top_4_single_cue$short_label

[1] "Ig log"   "I+Ig log" "G log"    "I log"   

Dual cue

dual_cue_dyn_30.df %>% group_by(id, id_b) %>% summarise(max_time = max(time))

`summarise()` has grouped output by 'id'. You can override using the `.groups` argument.

#————-Plot disease curves—————–# # Single cue

dual cue

#———area of curve vs fitness—————# # function to calculate area within curve

get_dc_area <- function(df){
  x <- df$total
  y <- df$R
  chull(x,y)->i
  return(areapl(cbind(x[i],y[i])))
}

single cue area

# split df
si_dc.ls <- split(si_dc.df, si_dc.df$id)

# get area
si_dc.area <- cbind.data.frame(area = as.numeric(lapply(si_dc.ls, get_dc_area)), id = names(lapply(si_dc.ls, get_dc_area)))

# join with fitness
si_opt_area.df <- si_opt.df %>% 
  filter(cue != "t") %>% 
  left_join(si_dc.area, by = "id") %>% 
  mutate(fitness_norm = fitness_20/9.883602) ## normalize fitness (use fitness obtained using time df=9 given that we are plotting dual cue and single on the same graph!)

# sanity check area. The area should be on the order if we assume a rectangle!
si_dc.df %>% 
  group_by(id) %>% 
  summarise(R_diff = max(R)-min(R),
            total_diff = max(total)-min(total),
            product = R_diff*total_diff)

dual cue

fit GLM

# fit lm
si_lm <- lm(fitness_norm ~ area, data = si_opt_area.df)
dual_lm <- lm(fitness_norm ~ area, data = dual_cue_f_final_area.df)

# get prediction with 95% CI
si_lm.ci = predict(si_lm, interval = "confidence") %>% cbind.data.frame(area = si_opt_area.df$area)
dual_lm.ci = predict(dual_lm, interval = "confidence") %>% cbind.data.frame(area = si_opt_area.df$area)

# look at R^2
summary(si_lm)$r.squared ## 0.1765248

[1] 0.1765248

summary(dual_lm)$r.squared ## 0.1053591

[1] 0.1053591

plot correlation

# single cue
si_corr_area.plt <- ggplot() +
  geom_point(data = si_opt_area.df, aes(x = area, y = fitness_norm, color = "Single cue", shape = "Single cue"), size = 3, alpha = 0.7) +
  geom_line(data = si_lm.ci, aes(x = area, fit)) +
  geom_ribbon(data = si_lm.ci, aes(x = area, ymin = lwr, ymax = upr), alpha = .15) +
  annotate(geom = "text", label = "R^2=0.18", x = 1.6*(10^11), y = 0.76) +
  labs(x = "Area", y = "Normalized fitness", shape = "Cue category", color = "Cue category") +
  scale_color_manual(values = c(orange)) +
  xlim(min(si_opt_area.df$area), max(dual_cue_f_final_area.df$area)) +
  theme_classic() +
  theme(legend.position = "none")

# dual cue
dual_corr_area.plt <- ggplot() +
  geom_point(data = dual_cue_f_final_area.df, aes(x = area, y = fitness_norm, color = "Dual cue", shape = "Dual cue"), size = 3, alpha = 0.7) +
  geom_line(data = dual_lm.ci, aes(x = area, fit)) +
  geom_ribbon(data = dual_lm.ci, aes(x = area, ymin = lwr, ymax = upr), alpha = .15) +
 annotate(geom = "text", label = "R^2=0.11", x = 1.6*(10^11), y = 0.76) +
  labs(x = "Area", y = "Normalized fitness", shape = "Cue category", color = "Cue category") +
  scale_color_manual(values = c(blue)) +
  xlim(min(si_opt_area.df$area), max(dual_cue_f_final_area.df$area)) +
  theme_classic() +
  theme(legend.position = "none")

#———assemble figure————#

si_dc_area.plt <- ggarrange(si_dc.plt, si_corr_area.plt, ncol = 2, nrow = 1, align = "hv", common.legend = T, labels = c("A", "B"))
dual_dc_area.plt <- ggarrange(dual_dc.plt, dual_corr_area.plt, ncol = 2, nrow = 1, align = "hv", common.legend = T, labels = c("C", "D"))

Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 2 rows containing missing values or values outside the scale range (`geom_line()`).

ggarrange(si_dc_area.plt, dual_dc_area.plt, nrow = 2, align = "v")

ggsave(units = "px", dpi = 300, width = 2550, height = 2000, filename = here("code_repository/figures/virulence.tiff"), bg = "white", scale = 1)

#=============================# # Validation of optimization #=============================# #—— process input data ——# # Process data

## get difference betweent fitness conferred by model produced by a random spline strategy "V1" vs optimized fitness
validation.df_p <- validation.df %>% 
  left_join(select(si_opt.df, id, fitness_20, short_label, long_label), by = c("id")) %>% 
  mutate(diff = fitness_20-V1,
         long_short_label = paste0(long_label, " (", short_label, ")"))

## no random strategy performed better!
validation.df_p %>% filter(diff<=0)

Plot

ggplot(validation.df_p) +
  geom_density(aes(x = diff), fill = "light grey") +
  facet_wrap(~long_short_label, scales = "free") +
  labs(x = "Fitness difference (Optimal-Random)", y = "Density") +
  theme_bw() +
  theme(legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) 

ggsave(units = "px", dpi = 300, width = 2550, height = 1500, filename = here("code_repository/figures/si_validation.tiff"), bg = "white", scale = 1)